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Article ; Online: Geospatial Management and Analysis of Microstructural Data from San Andreas Fault Observatory at Depth (SAFOD) Core Samples

Elliott M. Holmes / Andrea E. Gaughan / Donald J. Biddle / Forrest R. Stevens / Jafar Hadizadeh

ISPRS International Journal of Geo-Information, Vol 10, Iss 332, p

2021 Volume 332

Abstract: Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying ... ...

Abstract	Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and apply remote sensing techniques to the data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed GIS-based procedures for: 1. Spatially referenced visualization and storage of various microstructural data from core billets; 2. 3D modeling of billets and thin section positions within each billet, which serve as a digital record after irreversible fragmentation of the physical billets; and 3. Vector feature creation and unsupervised classification of a multi-generation calcite vein network from cathodluminescence (CL) imagery. Building on existing work which is predominantly limited to the 2D space of single thin sections, our results indicate that a GIS can facilitate spatial treatment of data even at centimeter to nanometer scales, but also revealed challenges involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies.
Keywords	Geographic Information Systems (GIS) ; remote sensing ; structural geology ; 3D visualization ; spatial analyses ; Geography (General) ; G1-922
Subject code	910
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Dasymetric modeling: A hybrid approach using land cover and tax parcel data for mapping population in Alachua County, Florida

Jia, Peng / Andrea E. Gaughan

Applied geography. 2016 Jan., v. 66

2016

Abstract: Spatial techniques and fine-scale geographic data may be combined in a variety of innovative ways to serve high-resolution population modeling efforts at local scales, which has been further facilitated by growing computation power and access to open- ... ...

Abstract	Spatial techniques and fine-scale geographic data may be combined in a variety of innovative ways to serve high-resolution population modeling efforts at local scales, which has been further facilitated by growing computation power and access to open-source spatial data. Previous work has highlighted the importance of a dasymetric approach to produce a parcel-based high-resolution gridded population surface (HGPS). In this study, we investigate the application of land-cover data integrated with the parcel-based HGPS to further improve the accuracy of the HGPS. Consideration is given to twelve combinations made by three land cover strategies (1- no land cover class, 2- five separate classes, and 3- three combined classes) and four property type strategies (1- seven types from an empirical study, 2- eight residential types, 3- seventeen types within Alachua County, and 4- twenty-five types within Florida). Results from different strategies are statistically compared with the most significant combination identified as three combined land-cover classes (heavy vegetation, 0–50% and >50–100% impervious surface) and with seven property types from the empirical study (single family, mobile family, multi-family (≥10 and <10 units), condominiums, mobile homes parks, and homes for the aged). A final data set named the Enhanced HGPS (E-HGPS) is created for Alachua County, Florida, with a distribution of population counts at the scale of individual housing units. This study highlights an innovative approach to incorporating land-cover and parcel data for the purpose of spatial population modeling, and holds potential to broaden the E-HGPS to a state or regional scope.
Keywords	data collection ; geography ; land cover ; models ; parks ; population distribution ; spatial data ; vegetation ; Florida
Language	English
Dates of publication	2016-01
Size	p. 100-108.
Publishing place	Elsevier Ltd
Document type	Article
ISSN	0143-6228
DOI	10.1016/j.apgeog.2015.11.006
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Evaluating the Accuracy of Gridded Population Estimates in Slums

Dana R. Thomson / Andrea E. Gaughan / Forrest R. Stevens / Gregory Yetman / Peter Elias / Robert Chen

Urban Science, Vol 5, Iss 48, p

A Case Study in Nigeria and Kenya

2021 Volume 48

Abstract: Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in ... ...

Abstract	Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.
Keywords	SDG11 ; urban ; deprivation ; informal settlement ; poverty ; mapping ; Geography. Anthropology. Recreation ; G ; Social Sciences ; H
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using Very-High-Resolution Multispectral Classification to Estimate Savanna Fractional Vegetation Components

Andrea E. Gaughan / Nicholas E. Kolarik / Forrest R. Stevens / Narcisa G. Pricope / Lin Cassidy / Jonathan Salerno / Karen M. Bailey / Michael Drake / Kyle Woodward / Joel Hartter

Remote Sensing, Vol 14, Iss 551, p

2022 Volume 551

Abstract: Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for ... ...

Abstract	Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for measuring landscape-level habitat structure and function. In this study, we address whether FVC estimates, stratified by dominant vegetation type, vary with different classification approaches applied to very-high-resolution small unoccupied aerial system (UAS)-derived imagery. Using Parrot Sequoia imagery, flown on a DJI Mavic Pro micro-quadcopter, we compare pixel- and segment-based random forest classifiers alongside a vegetation height-threshold model for characterizing the FVC in a southern African dryland savanna. Results show differences in agreement between each classification method, with the most disagreement in shrub-dominated sites. When compared to vegetation classes chosen by visual identification, the pixel-based random forest classifier had the highest overall agreement and was the only classifier not to differ significantly from the hand-delineated FVC estimation. However, when separating out woody biomass components of tree and shrub, the vegetation height-threshold performed better than both random-forest approaches. These findings underscore the utility and challenges represented by very-high-resolution multispectral UAS-derived data (~10 cm ground resolution) and their uses to estimate FVC. Semi-automated approaches statistically differ from by-hand estimation in most cases; however, we present insights for approaches that are applicable across varying vegetation types and structural conditions. Importantly, characterization of savanna land function cannot rely only on a “greenness” measure but also requires a structural vegetation component. Underscoring these insights is that the spatial heterogeneity of vegetation structure on the landscape broadly informs land management, from land allocation, wildlife habitat use, natural resource ...
Keywords	savannas ; vegetation composition ; Africa ; random forest classifier ; vegetation structure ; unoccupied aerial systems ; Science ; Q
Subject code	710
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Measuring the contribution of built-settlement data to global population mapping

Jeremiah J. Nieves / Maksym Bondarenko / David Kerr / Nikolas Ves / Greg Yetman / Parmanand Sinha / Donna J. Clarke / Alessandro Sorichetta / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

Social Sciences and Humanities Open, Vol 3, Iss 1, Pp 100102- (2021)

2021

Abstract: Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built- ...

Abstract	Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
Keywords	Urban ; Population ; Growth model ; Built ; Settlement ; Machine learning ; History of scholarship and learning. The humanities ; AZ20-999 ; Social sciences (General) ; H1-99
Subject code	910
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Implications for Tracking SDG Indicator Metrics with Gridded Population Data

Cascade Tuholske / Andrea E. Gaughan / Alessandro Sorichetta / Alex de Sherbinin / Agathe Bucherie / Carolynne Hultquist / Forrest Stevens / Andrew Kruczkiewicz / Charles Huyck / Greg Yetman

Sustainability, Vol 13, Iss 7329, p

2021 Volume 7329

Abstract: Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial ... ...

Abstract	Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.
Keywords	Sustainable Development Goals ; hazards ; Earth observations ; remote sensing ; demography ; urbanization ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	333
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape

Kyle D. Woodward / Narcisa G. Pricope / Forrest R. Stevens / Andrea E. Gaughan / Nicholas E. Kolarik / Michael D. Drake / Jonathan Salerno / Lin Cassidy / Joel Hartter / Karen M. Bailey / Henry Maseka Luwaya

Remote Sensing, Vol 13, Iss 4, p

Integrating Remote Sensing and Participatory Mapping

2021 Volume 631

Abstract: Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging ... ...

Abstract	Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas.
Keywords	remote sensing ; participatory mapping ; NTFP ; grazing ; random forest ; natural resources ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A fine-scale spatial population distribution on the High-resolution Gridded Population Surface and application in Alachua County, Florida

Jia, Peng / Andrea E. Gaughan / Youliang Qiu

Applied geography. 2014 June, v. 50

2014

Abstract: Geospatial techniques, using Geographic Information Systems and remote sensing data, have become more commonly used with dasymetric modeling of fine-scale demographic data. In this study, we apply a dasymetric approach using the Heuristic Sampling Method ...

Abstract	Geospatial techniques, using Geographic Information Systems and remote sensing data, have become more commonly used with dasymetric modeling of fine-scale demographic data. In this study, we apply a dasymetric approach using the Heuristic Sampling Method for 2010 parcel data to disaggregate population counts from the 2010 U.S. Census into a quadrilateral grid composed of 30 × 30 m cells covering the Alachua County, Florida. The final output, termed the High-resolution Gridded Population Surface (HGPS), is compared to a land cover-based population product (LCPP) and the detail of each product is assessed. Results suggest that the HGPS provides increased spatial heterogeneity and more detail in the boundaries of populated areas over the use of census blocks or land cover lots. For an example of the final output, we use a case study at the Cabot–Koppers Superfund Site to demonstrate the advantages of the HGPS over the LCPP. The HGPS is expected to serve as a more accurate input in various research fields, such as public health, crime analysis, and climate change. The approach outlined provides an improved means of producing spatially-explicit population grids where fine-scale ancillary data, such as parcel data, is available.
Keywords	case studies ; climate change ; crime ; geographic information systems ; geography ; land cover ; models ; population distribution ; public health ; spatial data ; spatial variation ; Florida
Language	English
Dates of publication	2014-06
Size	p. 99-107.
Publishing place	Elsevier Ltd
Document type	Article
ISSN	0143-6228
DOI	10.1016/j.apgeog.2014.02.009
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Gridded Population Maps Informed by Different Built Settlement Products

Fennis J. Reed / Andrea E. Gaughan / Forrest R. Stevens / Greg Yetman / Alessandro Sorichetta / Andrew J. Tatem

Data, Vol 3, Iss 3, p

2018 Volume 33

Abstract: The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded ... ...

Abstract	The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.
Keywords	gridded population distribution ; geography ; built areas ; remote sensing ; geographic information systems ; random forest ; regression ; binary dasymetric ; Bibliography. Library science. Information resources ; Z
Subject code	910 ; 333
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations

Jeremiah J. Nieves / Maksym Bondarenko / Alessandro Sorichetta / Jessica E. Steele / David Kerr / Alessandra Carioli / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

Remote Sensing, Vol 12, Iss 1545, p

2020 Volume 1545

Abstract: Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time- ... ...

Abstract	Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000–2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011–2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement ...
Keywords	Urban ; growth model ; forecast ; built ; settlement ; machine learning ; Science ; Q
Subject code	333
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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